Diffusion tensor imaging (DTI) is an MRI-based technique with great potential to enhance clinical diagnosis of pathology in structured tissue. In particular, DTI has shown promise in the area of neurological disease, exhibiting some sensitivity to identifying white-matter tumor extent, sclerotic lesions, and compression trauma in the spine. In clinical research, DTI has been used to map white matter fiber trajectories in the brain. Other pathology that may be favorably characterized with DTI includes liver and renal diseases, and potentially other tissues and conditions.
Despite its utility, the routine diagnostic application of DTI remains in its infancy. Reasons for this include that, considering the large amount of information that DTI provides as compared to an ordinary MR sequence, the clinical adoption of standardized protocols is lagging. During a DTI sequence, a series of images are generated by applying gradient magnetic fields along particular directions, to measure the directional dependence of diffusion. DTI reduces this series of measurements to a tensor at every image location, with each eigenvalue (sometimes referred to as “e-value”) and eigenvector representing the apparent diffusion coefficient (ADC) values along principle axes of an ellipsoid. Precision of the measurements depends on the number of directions sampled and the choice of particular direction schemes. Furthermore, DTI measurements characterize tissue properties indirectly, including cellular size, orientation, heterogeneity, and cell permeability. Uncertainty persists in the understanding of how DTI measures correlate with these tissue characteristics and how they change with disease.
Procedures for quality assurance (QA) and for estimation/measurement of systematic uncertainty have yet to be developed for DTI. In comparison with a single intensity value per voxel measured using a T1-weighted MR sequence, the end-product of a DTI series is six values to define a tensor within a given reference frame, where the frame is defined by three orthogonal vectors. As expected, the effect of noise on DTI data is more complicated than for routine clinical images, leading to a systematic bias that depends on SNR (signal to noise ratio). In an effort to eliminate image distortion inherent to the echo-planar imaging sequences predominantly used in the clinic and to migrate to higher-resolution imaging, parallel imaging has been incorporated with DTI. Unfortunately, while the array coils necessary for parallel MR scanning systems show improved SNR overall, their use changes the spatial properties of the noise distribution over the image. This effect of non-uniformity in the spatial sensitivity of surface coils is enhanced further using parallel imaging, leading to regions where noise may be higher or have variable spatial correlation, decreasing sensitivity within these regions.
One known technique for DTI data synthesis involves mixing signals from different parts of magnitude images (i.e., non-complex data such as a grayscale image with pixel values from 0 to some maximum value) to provide ground truth for tractography algorithms.